Population Balance Models (PBMs), a class of integro partial differential equations, are utilized for simulating dynamics of numerous particulate systems. PBMs describe the time evolutions and distributions of many particulate processes and their efficient and quick simulation are critical for enhanced process control and optimization, especially for real-time applications. However, their intensive computational resource requirement is largely a prohibitive factor in the utility of PBMs for control and optimization. This paper describes how distributed computing systems may be leveraged to execute PBM-based simulations thus achieving time savings, using MATLAB's Distributed Computing Toolbox. A parallel computing algorithm was developed for a three dimensional and four dimensional population balance model with built-in constructs such as SPMD that ran efficiently on a cluster of two quad-core machines linked via a gigabit ethernet cable. Speedup of 6.2 and 5.7 times were achieved with 8 workers, over an un-parallelized code, for a 3 and 4 dimensional PBM respectively. Evaluations on work efficiency and scalability further affirm the algorithms' respectable performance on larger clusters despite significant memory transfer overheads.
All Science Journal Classification (ASJC) codes
- Chemical Engineering(all)
- Distributed computing
- Multi-dimensional Population Balance Model
- Parallel computing